Instructions to use flax/StudioGhibli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use flax/StudioGhibli with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("flax/StudioGhibli", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- 3bfe8c1d3c594a4180c1b4fc646b7409fbac3618777cee0f75a436bd6282ceb9
- Size of remote file:
- 492 MB
- SHA256:
- d4de5c7fa0a1568d44f82b40ea4e9ef543b45396559cba308e438e3cfcad754c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.